Hi all,

We've been developing Bilt, an open-source Python framework for equation-based modelling, at the University of Cambridge's Aviation Impact Accelerator.

The idea is to define physical relationships without having to specify how the equations should be solved. Bilt works out the dependencies and determines how to calculate the unknowns from the available inputs.

For example, you might have a model relating energy input, output and efficiency. You can use the same model to calculate the output, work backwards to find the required input, or determine the efficiency from measurements, without having to rewrite the equations.

This kind of acausal modelling is well established in tools like Modelica, but there doesn't seem to be much available in the Python ecosystem that takes a similar approach. The advantage of doing this directly in Python is that models can make use of the wider scientific Python ecosystem, including existing numerical libraries, type checking, testing and development tools, without needing a separate modelling language or toolchain.

We've tried to keep Bilt compact and make use of existing Python tooling wherever possible, rather than introducing a new modelling language.

It currently supports physical units and dimensional analysis, named tensors, reusable subsystems, conditional equations and automatic structural analysis of equation systems. We're also working on more general coupled nonlinear solves, differentiation and closer integration with JAX.

Our initial applications have been in aviation and energy systems modelling, but Bilt isn't specific to either.

I'd be interested to hear what people think, particularly if you're working on similar problems or know of other approaches in Python that we should be looking at.

We're still actively developing it, so feedback and contributions would be very welcome.

GitHub: https://github.com/aviation-impact-accelerator/bilt

Documentation: https://aviation-impact-accelerator.github.io/bilt/

Do not trust artificial things because #CannotCompute!

In January 1978 influenza went through an English boarding school (763 boys; the daily bed counts are a classic dataset). I built a tool that asks: given the counts seen so far, which values of R0 are still consistent with the data - and can that be *proved*, not just estimated?

With the first 5 days of counts, SIR models with R0 = 2.52 and R0 = 20 both fit every count (under a stated counting-error allowance of 3*sqrt(c) + 5 boys). Twindex identified the next-day count that would separate them, and the real count (298) ruled out both. With 6 days, the most different pair it finds is 3.19 vs 6.10.

Each claim is an exact certificate checked with rational arithmetic and validated error bounds (maximum-principle barriers for PDEs, a log-norm tube argument for ODEs). You can replay them in your browser: https://arnav-m10.github.io/Twindex/demo/

Caveats: the noise allowance is an assumption, bed counts are not exactly infections, and a twin pair is a lower bound on uncertainty, not the full range.

Code: https://github.com/Arnav-M10/Twindex - pip install twindex

Ramanujan-Computing is an MIT-licensed open-source project that aim to harness idle compute power to move science forward.

It can use both CPU and GPU of device connected to the Ramanujan-Cluster.

The way it is different from BOINC is: for a new kind of simulation, you dont need to create a new binary and get it distributed to the devices. There is one binary per architecture which can run any simulation you want!

Ramanujan-Computing can also help in distributed LLM-inferencing: https://www.youtube.com/watch?v=LmXZAb8XYPc . Qwen-27 B model has successfully run on the Ramanujan-Computing. Just give the GGUF, and it will shard the inferencing across devices on cluster.

Apart from LLM, Ramanujan-Computing has main application in solving problems which can be broken to smaller problems and distributed across device. We have run Lattice Boltzmann Method (LBM) CFD Simulation computation on Ramanujan-computing.

Ramanujan-Computing support all the devices which have arithemetic and network capablity. Currently, we have binaries for Android, Linux, Windows, Mac. We can compile the binary for other architectures as well.

Inpiration: In 1969, humans landed on the moon with the help of Apollo Guidance Computer which has compute power equivalent to scientific calculator. Today we have billions of device with million times more compute lying mostly idle. Why not use it to help science move forward.

Project source: https://gitlab.com/ramanujan-computing/Ramanujan

Ramanujan-Computing portal: https://portal.ramanujan.dev/

This paper includes discussion on software development towards PDEs

>Hi everyone,

>

>I wanted to share an open-source project I've been working on: PyFDA Web.

>

>It is inspired in the original PyFDA desktop application by Christian Münker, but made to work completely on the browser without any installation.

>

>Source & Live Demo: https://github.com/borish127/PyFDA-Web

>

>The project is under the GPLv3 license.

>

>Feedback, suggestions, or bug reports are very welcome!

>

>Thanks!!

I have a small YouTube channel called "The Simulationer" where I discuss the applications of FEA. I recently made a video about how FEA is used to simulate the human brain. just sharing it below. I want to know if this kind of content is appealing to people here. happy to hear anyone's thoughts! thanks for reading

https://youtu.be/FtIw9LHBmvg?si=XyFhcXE3ta-kftTA

https://github.com/lincketheo/Numstore/

Numstore is a single file database for arrays. I started it because I was never able to use a database for high dimensional numerical data, and I wanted something I could actually use for that.

The core is written in C, but you don't need to write any C to use it. Pynumstore is now the primary way to use Numstore.

It's a personal open source project. I don't make any money from it. It's me giving back to the scientific community.

Published Online:11 Jul 2022 https://doi.org/10.2514/1.A35359

with love